/// <summary>
/// Get normal (Gaussian) random sample with mean 0 and standard deviation 1
/// </summary>
/// <returns></returns>
public static double GetNormal()
{
// Use Box-Muller algorithm
double u1 = GenerateUniform.GetUniform();
double u2 = GenerateUniform.GetUniform();
double r = Math.Sqrt(-2.0 * Math.Log(u1));
double theta = 2.0 * Math.PI * u2;
return(r * Math.Sin(theta));
}
public override double ComputeValue()
{
if (!double.IsNaN(this.Min))
{
if (!double.IsNaN(this.Max))
{
return(GenerateUniform.GetUniform(this.Min, this.Max));
}
return(this.Min + GenerateUniform.GetUniform());
}
return(GenerateUniform.GetUniform());
}
/// <summary>
/// Get gamma random sample with parameters
/// </summary>
/// <param name="shape"></param>
/// <param name="scale"></param>
/// <returns></returns>
public static double GetGamma(double shape, double scale)
{
// Implementation based on "A Simple Method for Generating Gamma Variables"
// by George Marsaglia and Wai Wan Tsang. ACM Transactions on Mathematical Software
// Vol 26, No 3, September 2000, pages 363-372.
double d, c, x, xsquared, v, u;
if (shape >= 1.0)
{
d = shape - 1.0 / 3.0;
c = 1.0 / Math.Sqrt(9.0 * d);
for (; ;)
{
do
{
x = GenerateNormal.GetNormal();
v = 1.0 + c * x;
}while (v <= 0.0);
v = v * v * v;
u = GenerateUniform.GetUniform();
xsquared = x * x;
if (u < 1.0 - .0331 * xsquared * xsquared || Math.Log(u) < 0.5 * xsquared + d * (1.0 - v + Math.Log(v)))
{
return(scale * d * v);
}
}
}
else if (shape <= 0.0)
{
string msg = string.Format("Shape must be positive. Received {0}.", shape);
throw new ArgumentOutOfRangeException(msg);
}
else
{
double g = GetGamma(shape + 1.0, 1.0);
double w = GenerateUniform.GetUniform();
return(scale * g * Math.Pow(w, 1.0 / shape));
}
}